Geo-social Queries Research Articles

Query Processing of Geosocial Data in Location-Based Social Networks

The increasing use of social media and the recent advances in geo-positioning technologies have produced a great amount of geosocial data, consisting of spatial, textual, and social information, to be managed and queried. In this paper, we focus on the issue of query processing by providing a systematic literature review of geosocial data representations, query processing methods, and evaluation approaches published over the last two decades (2000–2020). The result of our analysis shows the categories of geosocial queries proposed by the surveyed studies, the query primitives and the kind of access method used to retrieve the result of the queries, the common evaluation metrics and datasets used to evaluate the performance of the query processing methods, and the main open challenges that should be faced in the near future. Due to the ongoing interest in this research topic, the results of this survey are valuable to many researchers and practitioners by gaining an in-depth understanding of the geosocial querying process and its applications and possible future perspectives.

Temporal Geo-Social Personalized Keyword Search Over Streaming Data

The unprecedented rise of social media platforms, combined with location-aware technologies, has led to continuously producing a significant amount of geo-social data that flows as a user-generated data stream. This data has been exploited in several important use cases in various application domains. This article supports geo-social personalized queries in streaming data environments. We define temporal geo-social queries that provide users with real-time personalized answers based on their social graph. The new queries allow incorporating keyword search to get personalized results that are relevant to certain topics. To efficiently support these queries, we propose an indexing framework that provides lightweight and effective real-time indexing to digest geo-social data in real time. The framework distinguishes highly dynamic data from relatively stable data and uses appropriate data structures and a storage tier for each. Based on this framework, we propose a novel geo-social index and adopt two baseline indexes to support the addressed queries. The query processor then employs different types of pruning to efficiently access the index content and provide a real-time query response. The extensive experimental evaluation based on real datasets has shown the superiority of our proposed techniques to index real-time data and provide low-latency queries compared to existing competitors.

Nearest close friend search in geo-social networks

The proliferation of GPS-enabled devices has led to the development of location-based social network services such as Facebook, Twitter, and Foursquare. Users of these services not only make new friends but also post various content that contains their location. Although the existing services have continued to improve, they are still weak in handling some situations. If some users want to make a new friend, for example, they could manually search for the potential friends among the acquaintances of their friends by considering both spatial proximity and social closeness one by one. However, conventional studies have insufficiently tackled this problem yet.In this paper, we define a novel type of geo-social query called the k-Nearest ℓ-Close Friends query, which retrieves the k nearest data objects from among the ℓ-hop friends of the query user. We also propose three approaches for processing a kℓ-NCF query: Neighboring Cell Search, Friend-Cell Search, and Personal-Cell Search. In addition, we develop an efficient method of index update for supporting dynamic environments. We conduct a variety of experiments on synthetic and real data sets to evaluate and compare our methods.

Modeling and Querying Moving Objects with Social Relationships

Current moving-object database (MOD) systems focus on management of movement data, but pay less attention to modelling social relationships between moving objects and spatial-temporal trajectories in an integrated manner. This paper combines moving-object database and social network systems and presents a novel data model called Geo-Social-Moving (GSM) that enables the unified management of trajectories, underlying geographical space and social relationships for mass moving objects. A bulk of user-defined data types and corresponding operators are also proposed to facilitate geo-social queries on moving objects. An implementation framework for the GSM model is proposed, and a prototype system based on native Neo4J is then developed with two real-world data sets from the location-based social network systems. Compared with solutions based on traditional extended relational database management systems characterized by time-consuming table join operations, the proposed GSM model characterized by graph traversal is argued to be more powerful in representing mass moving objects with social relationships, and more efficient and stable for geo-social querying.

Geo-Social K-Cover Group Queries for Collaborative Spatial Computing

With the rapid development of location-aware mobile devices, ubiquitous Internet access and social computing technologies, lots of users’ personal information, such as location data and social data, has been readily accessible from various mobile platforms and online social networks. The convergence of these two types of data, known as geo-social data , has enabled collaborative spatial computing that explicitly combines both location and social factors to answer useful geo-social queries for either business or social good. In this paper, we study a new type of Geo-Social K-Cover Group (GSKCG) queries that, given a set of query points and a social network, retrieves a minimum user group in which each user is socially related to at least $k$ other users and the users’ associated regions (e.g., familiar regions or service regions) can jointly cover all the query points. Albeit its practical usefulness, the GSKCG query problem is NP-complete. We consequently explore a set of effective pruning strategies to derive an efficient algorithm for finding the optimal solution. Moreover, we design a novel index structure tailored to our problem to further accelerate query processing. Extensive experiments demonstrate that our algorithm achieves desirable performance on real-life datasets.

A general framework for geo-social query processing

The proliferation of GPS-enabledmobile devises and the popularity of social networking have recently led to the rapid growth of Geo-Social Networks (GeoSNs). GeoSNs have created a fertile ground for novel location-based social interactions and advertising. These can be facilitated by GeoSN queries, which extract useful information combining both the social relationships and the current location of the users. This paper constitutes the first systematic work on GeoSN query processing. We propose a general framework that offers flexible data management and algorithmic design. Our architecture segregates the social, geographical and query processing modules. Each GeoSN query is processed via a transparent combination of primitive queries issued to the social and geographical modules. We demonstrate the power of our framework by introducing several "basic" and "advanced" query types, and devising various solutions for each type. Finally, we perform an exhaustive experimental evaluation with real and synthetic datasets, based on realistic implementations with both commercial software (such as MongoDB) and state-of-the-art research methods. Our results confirm the viability of our framework in typical large-scale GeoSNs.